12/2/2023 0 Comments Meal tracking websiteNorris admits that SnapCalorie’s algorithm may be biased toward American food, since the team collected most of the initial training data in the U.S. This isn’t possible for food, because people are very inaccurate at visually estimating portion size, so you can’t have people label the images after the fact.” “The traditional pipeline for training an AI model is to download public web images, have people label the images and then train the model to predict those labels. “We made sure these had all of the diverse and challenging conditions you’d see in the real world and we weighed out every single ingredient on a scale,” Norris said. soups, burritos, oils, “mystery sauces” and more - using a robotic rig. The algorithm’s reported strong performance comes from its unique training dataset of 5,000 meals, Norris says, which SnapCalorie created by taking thousands of photos of each meal - e.g. The results can be logged in SnapCalorie’s food journal or exported to fitness-tracking platforms like Apple Health. Using the algorithm, SnapCalorie both identifies the types of food in a photo and measures the portion size of each to estimate the caloric content. So how’s SnapCalorie improved? Beyond the use of depth sensors and reviewers, Norris points to an algorithm that the company developed that can ostensibly outperform a person at estimating a food’s calories. One 2020 study comparing some of the more popular AI-based calorie counters found that the most accurate - Calorie Mama - was only right about 63% of the time. There’s a lot of skepticism in the health industry around photo-driven calorie estimating tools - and for good reason. “There are other apps capable of using AI to do photo-based meal tracking, but none of them help with portion size estimation - the most important part to reduce error.” “On average, the team is able to reduce the caloric error to under 20%,” Norris says. But what makes SnapCalorie different, Norris claims, is its use of depth sensors on supported devices for measuring portion size and a team of human reviewers for “an added layer of quality.” Apps such as Calorie Mama, Lose It, Foodadvisor and Bite.AI have all attempted the feat - to varying degrees of success. To be clear, SnapCalorie isn’t the first computer vision-based app for calorie counting. “ SnapCalorie improves on the status quo by combining a variety of new technologies and algorithms.” “Human beings are terrible at visually estimating the portion size of a plate of food,” Norris said. The company previously raised $125,000 from unidentified investors in a pre-seed round. This month, SnapCalorie raised $2 million in funding from investors including Accel, Index Ventures, former CrossFit CEO Eric Roza and Y Combinator. SnapCalorie, powered by AI, attempts to get an accurate calorie count and macronutrient breakdown of a meal from a single photo taken with a smartphone. So several years ago, Norris teamed up with Scott Baron, a systems engineer in the aerospace industry, to launch a health-focused startup called SnapCalorie. He co-founded Google Lens, Google’s computer vision-powered app that brings up information related to the objects it identifies. While working at Google, Wade Norris wanted to create a project that could positively impact people’s lives.
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